A HandBook Dictionary On DataOps And Its Importance

A HandBook Dictionary on DataOps
and Its Importance
Big giants like Google and Amazon, release software quite often in a day!
Reason? They started to implement DevOps, which helped them improve
upon their quality of codes and reduced their product cycles. Optimizing and
releasing codes swiftly was once a pipedream for most of the organizations.
However, the end-to-end cycle time has greatly been reduced for the
organizations that have already started implementing the practices and
making value out of it.
After observing the success of Big Giants, companies want to get into the
process ending with -Ops treatment. They want to embrace the
revolutionary change the DataOps practices are bringing into the process.
DataOps, under its umbrella, covers Agile methodologies, DevOps, and Lean

Manufacturing processes and collaboratively helps in focusing on
communication improvement, integration, and automation of the data
coming in the data pipeline.
Nick Heudecker, an analyst at Gartner, confirmed that the implementation of
DataOps in the ​Test Environment Management Tool ​automates the data
flow between the managers and consumers. Additionally, it mitigates the
chances of any miscommunication between the makers and the buyers. He
further added that it is a people-driven practice first and then a
technology-oriented. The inconsistency, inflexibility, bottlenecks, long cycles,
and a waste of time almost becomes negligible with the implementation.
It is observed that nearly 75 % of an employee’s day is unproductive
because of unplanned and more work scenarios. It does happen with the
organizations that resources are there. However, they still feel the need to
hire more to improve the overall productivity of the process. Such scenarios
are a suitable example of poor business processes.
It was quite a surprise for everyone to know when Amazon declared that
their team releases 50,000,000 codes every year, while for others, it
requires a minimum of 6 months for the data team to deliver a 20-line SQL

change. Imagine the wonders this implementation can bring to your
company if followed well.
DataOps helps in making procurement and storage of data efficient and fast.
It also gives real-time insights into the large volume of data collected
automatically by the tools. It parallelly works with different processes related
to data handling, including the ​DevSecOps ​practices and quickens the
software release time, thereby improving the quality of products.
Having said that, there are challenges and troubles faced by the
management in handling data for quality analysis. If not implemented in the
right manner, the collected data loses its value, and the delivery time starts
fluctuating. And this enforces the data management team to remain on their
toes and ensure that all the queries are resolved on time, and no process is
delayed beyond expectations.
Adding to this, the data in the pipeline is growing, and so is the requirement
and expectations from data analytics, scientist, and data-hungry
applications. Also, the data is received in different ways through different
platforms that demand more control over the system in order to identify the
loopholes.
Some daunting challenges are bad quality and manual processes.
Let’s have a look into them briefly.

Bad data quality:
The entire data loses its credibility if the collection of data is badly
performed. The whole program and the team is left in jeopardy if the data
formats are different and don’t match with the requirement. Various data
types and formats can lead to errors like duplication of entries, scheme
change, and input failures. When this goes out of hand, it becomes difficult
for the team to know the root cause and trace the error. Additionally,
constant and regular updates in the data pipeline mess up the situation
more. Coping up with these changes is a tad difficult and time-consuming
task for the organizations.

Manual Processes:
Manual integration of testing and analytics is a tedious and time-consuming
process. It takes hours and effort to analyze the data and come out with
meaningful data insights. The team involved with the analysis has to commit
more and make watchful steps without a single compromisation. Hence to
overcome these challenges, a tool wouldn’t suffice; instead, you need to
bring change in the underlying processes involved with the data
management.
DataOps, with its agile methodology, helps organizations overcome hurdles
and data management complexities without any compromisation. It focuses
mainly on data integration, cooperation, collaboration, communication,
measurement, and automation. This speed of process reduces the life cycle
of product delivery and sets up a clear transparent platform for
communication between engineers, data scientists, It and the Quality
assurance team.

DataOps Implementation:
Just a few minor changes in the on-going process helps in setting up
DataOps effectively into the organization. It mitigates manual errors and
efforts, thereby saving a lot of time. The implementation also notifies the
company if any projection is done or any security alert is detected. It keeps
the data intact in high quality and gives ultimate control over statistical
processes.
Implementation of these practices keeps the organization’s working culture
structured, boost reusability while supporting multi-developer environments.
It also facilitates customized version control over tools and systems, which
further will save a lot of development time and also speed up the analytics
related to the process. DataOps provide the utmost flexibility in dealing with
the data analytics pipeline. With minimal changes in the processes, DataOps
enables getting desired results in the system.
Are you excited to streamlines the processes using class-apart tools and
automating the workflow within the organization? The processes also impact
the production environment and keep a check on the quality of data received
in the pipeline.
You get live insights into the data and report generation, which enables
developers and stakeholders to speed up the process and thereby reduce the

product delivery time. DataOps is a promising phenomenon that evaluates
each and every step involved in the process. Not just a single step, but the
whole process continuously participates in making an organization
DataOps-compliant.

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Website:- https://www.enov8.com